ICDAR2023 Competition on Detection and Recognition of Greek Letters on Papyri

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Homer, Ilias(C) Staatliche Museen zu Berlin, Ägyptisches Museum und Papyrussammlung

This competition investigates the performance of glyph detection and recognition on a very challenging type of historical document: Greek papyri. The detection and recognition of Greek letters on papyri is a preliminary step for computational analysis of handwriting that can lead to major steps forward in our understanding of this major source of information on Antiquity. It can be done manually by trained papyrologists. It is however a time-consuming task that would need automatising. We provide two different tasks: localization and classification or classification only.The document images are provided by several institutions and are representative of the diversity of book hands on papyri (a millennium time span, various script styles, provenance, states of preservation, means of digitization and resolution).

Homer, Ilias 1, 54–64, 71–104, 114–123, 131–164, 412–433, 456–465, 494–534, 537–590, 602–609. 1. – 2. Jh. n.Chr.

Tasks

  • Glyph (character) localization
  • Character classification

Timeline

  • March 20th: publication of the test data
  • March 31st:
    • submission of the results
    • submission of a brief (~1 paragraph) description of the method

Results should be submitted either in the same format as the training ground truth, or in a CSV format along with a text file describing each column.

Competition results will be announced at the conference.

Data

The training data can be downloaded at the following address:

https://faubox.rrze.uni-erlangen.de/getlink/fi9GsXoxUHagRAjApzgk7ywo/HomerCompTraining.zip

The data is composed of images, and JSON files in the COCO format. The evaluation will be done using the average precision (AP) defined by COCO for the localization, and the percentage of correct answers for the classification.

Baseline

Coming soon

Registration

We would like people intending to participate to register for organizational purpose. Should any modification to the training data be done, we will inform the participants.

To register, either send an e-mail to mathias.seuret AT fau DOT de, or fill the following form:

https://forms.gle/4Xmk9qZRd5qRs8zM9

Organizers

  • Isabelle Marthot-Santaniello
  • Stephen White
  • Olga Serbaeva Saraogi
  • Dalia Rodriguez-Salas
  • Vincent Christlein
  • Mathias Seuret